Utility of CT in differentiating liver metastases of well-differentiated gastroenteropancreatic neuroendocrine neoplasms from poorly-differentiated neuroendocrine neoplasms
Abstract
Objective: To determine the capability of dynamic enhanced computed tomography (CT) to differentiate liver metastases (LMs) of well-differentiated from poorly-differentiated gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs).
Methods: Patients with LMs of GEP-NENs who underwent dynamic enhanced CT examination in Peking University Cancer Hospital from January 2009 to October 2015 were included and data were retrospectively analyzed. We assessed the qualitative and quantitative CT features to identify the significant differentiating CT features of LMs of poorly-differentiated GEP-NENs from those of well-differentiated GEP-NENs using univariate analysis and a multivariate logistic regression model.
Results: The study included 22 patients with LMs of well-differentiated GEP-NENs and 32 patients with LMs of poorly-differentiated GEP-NENs. Univariate analysis revealed statistically significant differences between the LMs of well- and poorly-differentiated GEP-NENs in terms of feeding arteries (36.4% vs. 75.0%, χ2=8.061, P=0.005), intratumoral neovascularity (18.2% vs. 59.4%, χ2=9.047, P=0.003), lymphadenopathy (27.3% vs. 81.2%, χ2=15.733, P<0.001), tumor-to-aortic ratio in the hepatic arterial and portal venous phase (T-A/AP: 0.297±0.080 vs. 0.251±0.059, t=2.437, P=0.018; T-A/PVP: 0.639±0.138 vs. 0.529±0.117, t=3.163, P=0.003) and tumor-to-liver ratio in the hepatic arterial phase (T-L/AP: 1.108±0.267 vs. 0.907±0.240, t=2.882, P=0.006). The LMs of poorly-differentiated GEP-NENs showed more feeding arteries, more intratumoral neovascularity, more lymphadenopathy and a lower tumor-to-aortic ratio. Multivariate analysis suggested that intratumoral neovascularity [P=0.015, OR=0.108, 95% confidence interval (95% CI), 0.018–0.646], lymphadenopathy (P=0.001, OR=0.055, 95% CI, 0.009–0.323) and T-A/PVP (P=0.004, OR=5.3E–5, 95% CI, 0.000–0.044) were independent factors for differentiating LMs of poorly-differentiated from well-differentiated GEP-NENs.
Conclusions: Dynamic enhanced CT features (intratumoral neovascularity, lymphadenopathy and T-A/PVP) are useful in the pathological classification of LMs of GEP-NENs.
Keywords: Diagnosis; gastroenteropancreatic neuroendocrine neoplasm; neoplasm grading; tomography, X-ray computed
Methods: Patients with LMs of GEP-NENs who underwent dynamic enhanced CT examination in Peking University Cancer Hospital from January 2009 to October 2015 were included and data were retrospectively analyzed. We assessed the qualitative and quantitative CT features to identify the significant differentiating CT features of LMs of poorly-differentiated GEP-NENs from those of well-differentiated GEP-NENs using univariate analysis and a multivariate logistic regression model.
Results: The study included 22 patients with LMs of well-differentiated GEP-NENs and 32 patients with LMs of poorly-differentiated GEP-NENs. Univariate analysis revealed statistically significant differences between the LMs of well- and poorly-differentiated GEP-NENs in terms of feeding arteries (36.4% vs. 75.0%, χ2=8.061, P=0.005), intratumoral neovascularity (18.2% vs. 59.4%, χ2=9.047, P=0.003), lymphadenopathy (27.3% vs. 81.2%, χ2=15.733, P<0.001), tumor-to-aortic ratio in the hepatic arterial and portal venous phase (T-A/AP: 0.297±0.080 vs. 0.251±0.059, t=2.437, P=0.018; T-A/PVP: 0.639±0.138 vs. 0.529±0.117, t=3.163, P=0.003) and tumor-to-liver ratio in the hepatic arterial phase (T-L/AP: 1.108±0.267 vs. 0.907±0.240, t=2.882, P=0.006). The LMs of poorly-differentiated GEP-NENs showed more feeding arteries, more intratumoral neovascularity, more lymphadenopathy and a lower tumor-to-aortic ratio. Multivariate analysis suggested that intratumoral neovascularity [P=0.015, OR=0.108, 95% confidence interval (95% CI), 0.018–0.646], lymphadenopathy (P=0.001, OR=0.055, 95% CI, 0.009–0.323) and T-A/PVP (P=0.004, OR=5.3E–5, 95% CI, 0.000–0.044) were independent factors for differentiating LMs of poorly-differentiated from well-differentiated GEP-NENs.
Conclusions: Dynamic enhanced CT features (intratumoral neovascularity, lymphadenopathy and T-A/PVP) are useful in the pathological classification of LMs of GEP-NENs.
Keywords: Diagnosis; gastroenteropancreatic neuroendocrine neoplasm; neoplasm grading; tomography, X-ray computed